Multi-Resolution Feature Fusion model for coal rock burst hazard recognition based on Acoustic Emission data

The Acoustic Emission (AE) technique is an important nondestructive testing method used to detect coal rock burst hazards in coal mine strata to ensure the safety of the lives of miners. It is, however, still difficult to accurately detect coal rock bursts due to the complex mechanism of AE propagation underground. Therefore, this study proposes a new Multi-Resolution Feature Fusion SVM recognition (MRFF-SVM) approach to compute a comprehensive feature vector for coal rock burst hazard recognition and forecasting. The proposed approach contains the following three improved processes: the Coiflet Wavelet Transform (CWT) to decompose AE waveforms into multiple perspectives for feature vectors extraction, the Multi-Resolution Feature Fusion (MRFF) method to fuse these selected feature vectors into an enhanced MRFF feature vector and the following Support Vector Machines (SVM) to recognize the coal rock burst conditions. Several innovative experiments are carried out to evaluate the coal rock conditions according to the safe, crisis and burst categories. The results indicate that the proposed MRFF feature vectors can retain AE signatures well to recognize coal rock conditions and forecast sandstone failure. Thus, MRFF-SVM provides an effective analysis tool for coal rock burst hazard detection.